The authors propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. This work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional generative adversarial network that is designed for the inference of three-dimensional volumetric data, the proposed model generates consistent and detailed results by using a novel temporal discriminator, in addition to the commonly used spatial one.